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Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors

With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as image processing and pattern recognition. Artifici...

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Autores principales: Kalita, Hirokjyoti, Krishnaprasad, Adithi, Choudhary, Nitin, Das, Sonali, Dev, Durjoy, Ding, Yi, Tetard, Laurene, Chung, Hee-Suk, Jung, Yeonwoong, Roy, Tania
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328611/
https://www.ncbi.nlm.nih.gov/pubmed/30631087
http://dx.doi.org/10.1038/s41598-018-35828-z
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author Kalita, Hirokjyoti
Krishnaprasad, Adithi
Choudhary, Nitin
Das, Sonali
Dev, Durjoy
Ding, Yi
Tetard, Laurene
Chung, Hee-Suk
Jung, Yeonwoong
Roy, Tania
author_facet Kalita, Hirokjyoti
Krishnaprasad, Adithi
Choudhary, Nitin
Das, Sonali
Dev, Durjoy
Ding, Yi
Tetard, Laurene
Chung, Hee-Suk
Jung, Yeonwoong
Roy, Tania
author_sort Kalita, Hirokjyoti
collection PubMed
description With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as image processing and pattern recognition. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal–oxide–semiconductor (CMOS) circuitry in the past. Recently, metal-insulator-transition materials have been used to realize artificial neurons. Although memristors have been implemented to realize synaptic behavior, not much work has been reported regarding the neuronal response achieved with these devices. In this work, we use the volatile threshold switching behavior of a vertical-MoS(2)/graphene van der Waals heterojunction system to produce the integrate-and-fire response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MoS(2), enabling large scale realization of these devices. These devices can emulate the most vital properties of a neuron, including the all or nothing spiking, the threshold driven spiking of the action potential, the post-firing refractory period of a neuron and strength modulated frequency response. These results show that the developed artificial neuron can play a crucial role in neuromorphic computing.
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spelling pubmed-63286112019-01-14 Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors Kalita, Hirokjyoti Krishnaprasad, Adithi Choudhary, Nitin Das, Sonali Dev, Durjoy Ding, Yi Tetard, Laurene Chung, Hee-Suk Jung, Yeonwoong Roy, Tania Sci Rep Article With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as image processing and pattern recognition. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal–oxide–semiconductor (CMOS) circuitry in the past. Recently, metal-insulator-transition materials have been used to realize artificial neurons. Although memristors have been implemented to realize synaptic behavior, not much work has been reported regarding the neuronal response achieved with these devices. In this work, we use the volatile threshold switching behavior of a vertical-MoS(2)/graphene van der Waals heterojunction system to produce the integrate-and-fire response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MoS(2), enabling large scale realization of these devices. These devices can emulate the most vital properties of a neuron, including the all or nothing spiking, the threshold driven spiking of the action potential, the post-firing refractory period of a neuron and strength modulated frequency response. These results show that the developed artificial neuron can play a crucial role in neuromorphic computing. Nature Publishing Group UK 2019-01-10 /pmc/articles/PMC6328611/ /pubmed/30631087 http://dx.doi.org/10.1038/s41598-018-35828-z Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kalita, Hirokjyoti
Krishnaprasad, Adithi
Choudhary, Nitin
Das, Sonali
Dev, Durjoy
Ding, Yi
Tetard, Laurene
Chung, Hee-Suk
Jung, Yeonwoong
Roy, Tania
Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors
title Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors
title_full Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors
title_fullStr Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors
title_full_unstemmed Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors
title_short Artificial Neuron using Vertical MoS(2)/Graphene Threshold Switching Memristors
title_sort artificial neuron using vertical mos(2)/graphene threshold switching memristors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328611/
https://www.ncbi.nlm.nih.gov/pubmed/30631087
http://dx.doi.org/10.1038/s41598-018-35828-z
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